Spotify’s API

While I was waiting for Spotify to send me my data, I started playing around with Spotify’s API by using the spotifyr package.

Vizualizing Genre

The spotifyr package allows you to pull up to 50 of your top artists and the genres associated with each of them. For this first part, I was curious about which genres appeared most frequently among my 50 most listened to artists.

## Warning: The `size` argument of `element_line()` is deprecated as of ggplot2 3.4.0.
## ℹ Please use the `linewidth` argument instead.

Song Features

I also discovered that Spotify’s API provides interesting metrics on all songs (ex. valence, danceability, liveness).

Average

First things first, I wanted to see the average of these metrics for all 2,000+ songs in one of my playlists (aka Tune Void).

##     danceability           energy         loudness      speechiness 
##       0.55626345       0.65857394      -7.19828373       0.05693734 
##     acousticness instrumentalness         liveness          valence 
##       0.25191763       0.05610170       0.18387893       0.52342722 
##            tempo 
##     121.98788817

Correlation Network

Next, I wanted to see how the features of the songs in this playlist related to each other. Correlation networks look cool, so I went with that.

## Correlation computed with
## • Method: 'pearson'
## • Missing treated using: 'pairwise.complete.obs'

Heatmap

Finally, I wanted to take the 50 most recently added songs from the Tune Void and see how those songs cluster together based on the metrics Spotify provides.

One Year of Streaming Data

[write later]

Alex’s Code

Tracks by Release Date